Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions

PURPOSE

This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation.

METHODS

This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy.

RESULTS

The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89).

CONCLUSION

Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

Radiomics and liver: Where we are and where we are headed?

Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.

Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma

OBJECTIVES

The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC).

METHODS

The B-mode ultrasound images of 465 patients with primary liver cancer were enrolled in model construction, comprising 264 HCCs, 105 ICCs, and 96 cHCC-ICCs, of which 50 cases were randomly selected to form an independent test cohort, and the rest of study population was assigned to a training and validation cohorts at the ratio of 4:1. Four deep learning models (Resnet18, MobileNet, DenseNet121, and Inception V3) were constructed, and the fivefold cross-validation was adopted to train and validate the performance of these models. The following indexes were calculated to determine the differential diagnosis performance of the models, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F-1 score, and area under the receiver operating characteristic curve (AUC) based on images in the independent test cohort.

RESULTS

Based on the fivefold cross-validation, the Resnet18 outperformed other models in terms of accuracy and robustness, with the overall training and validation accuracy as 99.73% (± 0.07%) and 99.35% (± 0.53%), respectively. Furthers validation based on the independent test cohort suggested that Resnet 18 yielded the best diagnostic performance in identifying HCC, ICC, and cHCC-ICC, with the sensitivity, specificity, accuracy, PPV, NPV, F1-score, and AUC of 84.59%, 92.65%, 86.00%, 85.82%, 92.99%, 92.37%, 85.07%, and 0.9237 (95% CI 0.8633, 0.9840).

CONCLUSION

Ultrasound-based deep learning algorithm appeared a promising diagnostic method for identifying cHCC-ICC, HCC, and ICC, which might play a role in clinical decision making and evaluation of prognosis.

Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics

The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.

Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images

Traditional Cholangiocarcinoma detection methodology, which involves manual interpretation of histopathological images obtained after biopsy, necessitates extraordinary domain expertise and a significant level of subjectivity, resulting in several deaths due to improper or delayed detection of this cancer that develops in the bile duct lining. Automation in the diagnosis of this dreadful disease is desperately needed to allow for more effective and faster identification of the disease with a better degree of accuracy and reliability, ultimately saving countless human lives. The primary goal of this study is to develop a machine-assisted method of automation for the accurate and rapid identification of Cholangiocarcinoma utilizing histopathology images with little preprocessing. This work proposes CholangioNet, a novel lightweight neural network for detecting Cholangiocarcinoma utilizing histological RGB images. The histological RGB image dataset considered in this research work was found to have limited number of images, hence data augmentation was performed to increase the number of images. The finally obtained dataset was then subjected to minimal preprocessing procedures. These preprocessed images were then fed into the proposed lightweight CholangioNet. The performance of this proposed architecture is then compared with the performance of some of the prominent existing architectures like, VGG16, VGG19, ResNet50 and ResNet101. The Accuracy, Loss, Precision, and Sensitivity metrics are used to assess the efficiency of the proposed system. At 200 epochs, the proposed architecture achieves maximum training accuracy, precision, and recall of 99.90%, 100%, and 100%, respectively. The suggested architecture's validation accuracy, precision, and recall are 98.40%, 100%, and 100%, respectively. When compared to the performance of other AI-based models, the proposed system produced better results making it a potential AI tool for real world application.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

The significance of the predominant component in combined hepatocellular-cholangiocarcinoma: MRI manifestation and prognostic value

PURPOSE

To investigate the significance of the predominant component of combined hepatocellular-cholangiocarcinoma (cHCC-CC) in terms of MRI manifestation and its potential prognostic value compared to hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).

MATERIALS AND METHODS

A total of 300 patients with chronic liver disease from two centers were retrospectively enrolled, including 100 surgically proven cases of cHCC-CC, HCC, and ICC each. Univariate and multivariate regression analyses were performed to identify independent predictors for distinguishing HCC-predominant cHCC-CC and ICC-predominant cHCC-CC from HCC and ICC, respectively. Diagnostic models were constructed based on the independent features. Recurrence-free survival (RFS) was estimated and compared between groups.

RESULTS

The predominant component was an independent predictor for RFS in cHCC-CC (hazard ratio = 1.957, P = 0.044). The presence of targetoid appearance (odds ratio(OR) = 10.907, P = 0.001), lack of enhancing capsule (OR = 0.072, P = 0.001) and arterial peritumoral enhancement (OR = 0.091, P = 0.003) were independent predictors suggestive of HCC-predominant cHCC-CC over HCC; their combination yielded an area under the curve of 0.756. No significant differences were observed in RFS between HCC-predominant cHCC-CC and HCC (P = 0.864). Male gender (OR = 4.049, P = 0.015), higher alpha fetoprotein (OR = 16.789, P < 0.001) and normal carbohydrate antigen 19-9 (OR = 0.343, P = 0.036) levels, presence of enhancing capsule (OR = 7.819, P < 0.001) and hemorrhage (OR = 23.526, P = 0.004), and lack of targetoid appearance (OR = 0.129, P = 0.005) and liver surface retraction (OR = 0.190, P = 0.021) were independent predictors suggestive of ICC-predominant cHCC-CC over ICC; their combination yielded an area under the curve value of 0.898. ICC-predominant cHCC-CC exhibited poorer survival with shorter RFS than ICC (P = 0.009).

CONCLUSION

The predominant histopathological component is closely related to the imaging manifestation of cHCC-CC; and more importantly, it plays a significant prognostic role, which may alter the RFS prognosis of cHCC-CC.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma.

METHODS

The PubMed database was searched for papers published in the English language no earlier than October 2022.

RESULTS

We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective.

CONCLUSIONS

It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative

OBJECTIVES

To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies.

METHODS

A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients.

RESULTS

A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1-13) for reader 1, 8 (22.2%, IQR 3-12) for reader 2, and 10 (27.8%; IQR 5-14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62-0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1).

CONCLUSIONS

Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.

Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend

Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.

Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

Promising role of liver transplantation in patients with combined hepatocellular-cholangiocarcinoma: a propensity score matching analysis

Background

Combined hepatocellular-cholangiocarcinoma (CHC) is a rare but vital heterogeneous histological subtype of primary liver cancer (PLC) with no standardized treatment strategy. This study aimed to preliminarily investigate the role of liver transplantation (LT) in CHC and develop a novel risk scoring model (RSM) to evaluate the benefits of transplantation.

Methods

The study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) in incidence or ratio was calculated utilizing the Joinpoint regression. Propensity score matching (PSM) was introduced to reduce the selection bias between groups. A novel RSM was developed based on the independent prognostic factors identified by the Cox regression model. The predictive performance of the RSM was compared with the Milan Criteria and the University of California, San Francisco (UCSF) Criteria, respectively.

Results

A total of 223 CHC patients were enrolled, and 60 (26.9%) of them received LT. The incidence-based mortality did not decrease between 2004 and 2015 (APC =1.7%, P=0.195). Although LT was considered an independent protective predictor for CHC, it showed a declining ratio from 33.3% in 2004 to 15.4% in 2015 (APC =-8.9%, P=0.012). The LT recipients had better outcomes than others who underwent hepatectomy or local destruction (P<0.05). Compared with other subtypes of PLC, the post-transplantation prognoses of CHC patients were similar to those with hepatocellular carcinoma (P>0.05) but significantly better than those with intrahepatic cholangiocarcinoma (ICC) (P<0.05). Based on the RSM (vascular invasion: 1 point; tumor size >2 cm: 1 point; multiple tumors: 2 points), patients were stratified into two prognostic subgroups: the low-risk (scoring ≤2) and the high-risk (scoring >2 or extrahepatic metastasis) groups. Patients in the low-risk group were more likely to benefit from LT. The predictive performance of the RSM outperformed the Milan and UCSF Criteria in both the training and validation sets.

Conclusions

Therapeutic strategies for CHC should be further improved. Patients with CHC should also be considered potential LT candidates. The novel RSM could be helpful to stratify patients and assist clinical decision-making.

Radiomics of Biliary Tumors: A Systematic Review of Current Evidence

Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729−0.900, accuracy = 0.69−0.83), tumor grading (AUC = 0.680−0.890, accuracy = 0.70−0.82), and survival (C-index = 0.673−0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice.

Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017

Background

The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC.

Methods

Between 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity.

Results

In the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort.

Conclusion

The proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00765-x.

Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions

Objective

To explore the value of machine learning-based magnetic resonance imaging (MRI) liver acceleration volume acquisition (LAVA) dynamic enhanced scanning for diagnosing hilar lesions.

Methods

A total of 90 patients with hilar lesions and 130 patients without hilar lesions who underwent multiphase dynamic enhanced MRI LAVA were retrospectively selected as the study subjects. The 10-fold crossover method was used to establish the data set, 7/10 (154 cases) data were used to establish the training set, and 3/10 (66 cases) data were used to establish the validation set to verify the model. The region of interest was extracted from MRI images using radiomics, and the hilar lesion model was constructed based on a convolutional neural network.

Results

There were significant differences in respiration and pulse frequency between patients with hilar lesions and without hilar lesions (P <0.05). The subjective scores of the images in the first three phases of dynamic enhanced scanning in the training set were higher than those in the validation set (P < 0.05). There was no significant difference between the training and validation set in the last three phases of dynamic enhanced scanning.

Conclusion

Machine learn-based MRI LAVA dynamic enhanced scanning for diagnosing hilar lesions has high diagnostic efficiency and can be used as an auxiliary diagnostic method.

Artificial intelligence and cholangiocarcinoma: Updates and prospects

Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.

DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma

OBJECTIVE

To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC).

METHODS

A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared.

RESULTS

Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone (p < 0.05).

CONCLUSION

The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies.

KEY POINTS

• The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.

Morbidity, Prognostic Factors, and Competing Risk Nomogram for Combined Hepatocellular-Cholangiocarcinoma

Background

Combined hepatocellular-cholangiocarcinoma (CHC) is a rare and heterogeneous histological subtype of primary liver cancer, which is still poorly understood. This study aimed to describe the epidemiological and clinical features, investigate the prognostic indicators, and develop a competing risk nomogram for CHC.

Methods

The study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) in incidence was calculated using the joinpoint regression. The nomogram was developed based on multivariate competing risk survival analyses and validated by calibration curves. Akaike information criterion, Bayesian information criterion, Harrell's C-index, and area under the receiver operating characteristic curves were obtained to compare prognostic performance. Decision curve analysis was introduced to examine the clinical value of the models.

Results

The overall incidence of CHC was 0.062 per 100,000 individuals in 2004 and 0.081 per 100,000 individuals in 2018, with an APC of 1.0% (P > 0.05). CHC displayed intermediate clinicopathological features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Race, tumor size, vascular invasion, extrahepatic invasion, distant metastasis, grade, surgery, and Metavir stage were confirmed as the independent predictors of cancer-specific survival. The constructed nomogram was well calibrated, which showed better discrimination power and higher net benefits than the current American Joint Committee on Cancer staging system. Patients with liver transplantation had better survival than those with hepatectomy, especially patients within the Milan Criteria (P=0.022 and P=0.015). There was no survival difference between liver transplantation and hepatectomy in patients beyond the Milan Criteria (P=0.340).

Conclusion

The morbidity of CHC remained stable between 2004 and 2018. The constructed nomogram could predict the prognosis with good performance, which was meaningful to individual treatment strategies optimization. CHC patients should also be considered as potential liver transplantation recipients, especially those within the Milan Criteria, but the finding still needs more evidence to be further confirmed.

Cholangiocarcinoma: new perspectives for new horizons

INTRODUCTION

Biliary tract cancer represents a heterogeneous group of malignancies characterized by dismal prognosis and scarce therapeutic options.

AREA COVERED

In the last years, a growing interest in BTC pathology has emerged, thus highlighting a significant heterogeneity of the pathways underlying the carcinogenesis process, from both a molecular and genomic point of view. A better understanding of these differences is mandatory to deepen the behavior of this complex disease, as well as to identify new targetable target mutations, with the aim to improve the survival outcomes. The authors decided to provide a comprehensive overview of the recent highlights on BTCs, with a special focus on the genetic, epigenetic and molecular alterations, which may have an interesting clinical application in the next future.

EXPERT OPINION

In the last years, the efforts resulted from international collaborations have led to the identification of new promising targets for precision medicine approaches in the BTC setting. Further investigations and prospective trials are needed, but the hope is that these new knowledge in cooperation with the new technologies and procedures, including bio-molecular and genomic analysis as well radiomic studies, will enrich the therapeutic armamentarium thus improving the survival outcomes in a such lethal and complex disease.

MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma

OBJECT

To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics.

METHOD

This study retrospectively analyzed 196 liver cancers: 33 cHCC-CC, 88 HCC and 75 CC. They had confirmed by pathological analysis in the Affiliated Hospital of Hebei University. MRI lesions were manually segmented by a radiologist.1316 features were extracted from MRI lesions by Pyradiomics. Useful features were retained through two-level feature selection to establish a classification model. Receiver operating characteristic (ROC), area under curve (AUC) and F1-score were used to evaluate the performance of the model.

RESULTS

Compared with low-order image features, the performance of the model based on high-order features was improved by about 10%. The model showed better performance in identifying HCC tumors during the delay phase (AUC = 0.91, sensitivity = 0.88, specificity = 0.89, accuracy = 0.89, F1-Score = 0.88).

CONCLUSION

The classification ability of cHCC-CC, HCC and CC can be further improved by extracting MRI high-order features and using a two-level feature selection method.

Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma

BACKGROUND

Combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CCA) is defined as a single nodule showing differentiation into HCC and intrahepatic cholangiocarcinoma and has a poor prognosis.

AIM

To develop a radiomics nomogram for predicting post-resection survival of patients with cHCC-CCA.

METHODS

Patients with pathologically diagnosed cHCC-CCA were randomly divided into training and validation sets. Radiomics features were extracted from portal venous phase computed tomography (CT) images using the least absolute shrinkage and selection operator Cox regression and random forest analysis. A nomogram integrating the radiomics score and clinical factors was developed using univariate analysis and multivariate Cox regression. Nomogram performance was assessed in terms of the C-index as well as calibration, decision, and survival curves.

RESULTS

CT and clinical data of 118 patients were included in the study. The radiomics score, vascular invasion, anatomical resection, total bilirubin level, and satellite lesions were found to be independent predictors of overall survival (OS) and were therefore included in an integrative nomogram. The nomogram was more strongly associated with OS (hazard ratio: 8.155, 95% confidence interval: 4.498-14.785, P < 0.001) than a model based on the radiomics score or only clinical factors. The area under the curve values for 1-year and 3-year OS in the training set were 0.878 and 0.875, respectively. Patients stratified as being at high risk of poor prognosis showed a significantly shorter median OS than those stratified as being at low risk (6.1 vs 81.6 mo, P < 0.001).

CONCLUSION

This nomogram may predict survival of cHCC-CCA patients after hepatectomy and therefore help identify those more likely to benefit from surgery.

Role of noninvasive imaging in the evaluation of intrahepatic cholangiocarcinoma: from diagnosis and prognosis to treatment response

INTRODUCTION

Intrahepatic cholangiocarcinoma is the second most common liver cancer. Desmoplastic stroma may be revealed as distinctive histopathologic findings favoring intrahepatic cholangiocarcinoma. Meanwhile, a range of imaging manifestations is often accompanied with rich desmoplastic stroma in intrahepatic cholangiocarcinoma, which can indicate large bile duct ICC, and a higher level of cancer-associated fibroblasts with poor prognosis and weak treatment response.

AREAS COVERED

We provide a comprehensive review of current state-of-the-art and recent advances in the imaging evaluation for diagnosis, staging, prognosis and treatment response of intrahepatic cholangiocarcinoma. In addition, we discuss precursor lesions, cells of origin, molecular mutation, which would cause the different histological classification. Moreover, histological classification and tumor microenvironment, which are related to the proportion of desmoplastic stroma with many imaging manifestations, would be also discussed.

EXPERT OPINION

The diagnosis, prognosis, treatment response of intrahepatic cholangiocarcinoma may be revealed as the presence and the proportion of desmoplastic stroma with a range of imaging manifestations. With the utility of radiomics and artificial intelligence, imaging is helpful for ICC evaluation. Multicentre, large-scale, prospective studies with external validation are in need to develop comprehensive prediction models based on clinical data, imaging findings, genetic parameters, molecular, metabolic, and immune biomarkers.

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Purpose

To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model.

Methods

Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared.

Results

Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2.

Conclusion

The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03183-9.

Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review

BACKGROUND AND OBJECTIVES

Cholangiocarcinoma (CCA) is one of the most aggressive human malignant tumors and is becoming one of the main factors of death and disability globally. Specifically, 60% to 70% of CCA patients were diagnosed with local invasion or distant metastasis and lost the chance of radical operation. The overall median survival time was less than 12 months. As a non-invasive diagnostic technology, medical imaging consisting of computed tomography (CT) imaging, magnetic resonance imaging (MRI), and ultrasound (US) imaging, is the most effectively and commonly used method to detect CCA. The computer auxiliary diagnosis (CAD) system based on medical imaging is helpful for rapid diagnosis and provides credible "second opinion" for specialists. The purpose of this review is to categorize and review the CAD technique of detecting CCA based on medical imaging.

METHODS

This work applies a four-level screening process to choose suitable publications. 125 research papers published in different academic research databases were selected and analyzed according to specific criteria. From the five steps of medical image acquisition, processing, analysis, understanding and verification of CAD combined with artificial intelligence algorithms, we obtain the most advanced insights related to CCA detection.

RESULTS

This work provides a comprehensive analysis and comparison analysis of the current CAD systems of detecting CCA. After careful investigation, we find that the main detection methods are traditional machine learning method and deep learning method. For the detection, the most commonly used method is semi-automatic segmentation algorithm combined with support vector machine classifier method, combination of which has good detection performance. The end-to-end training mode makes deep learning method more and more popular in CAD systems. However, due to the limited medical training data, the accuracy of deep learning method is unsatisfactory.

CONCLUSIONS

Based on analysis of artificial intelligence methods applied in CCA, this work is expected to be truly applied in clinical practice in the future to improve the level of clinical diagnosis and treatment of it. This work concludes by providing a prediction of future trends, which will be of great significance for researchers in the medical imaging of CCA and artificial intelligence.

Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival

OBJECTIVE

Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis.

METHODS

A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8⁺ T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively.

RESULTS

Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05).

CONCLUSION

The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients.

KEY POINTS

• The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.

Applications of artificial intelligence in pancreatic and biliary diseases

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.

The clinical characteristics and prognostic factors of combined Hepatocellular Carcinoma and Cholangiocarcinoma, Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma after Surgical Resection: A propensity score matching analysis

Background: Clinical characteristics and prognosis among combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CC) with HCC and intrahepatic cholangiocarcinoma (ICC) were inconsistent in previous studies. The aim of this study was to compare postoperative prognosis among cHCC-CC, HCC and ICC, and investigated the prognostic risk factor of cHCC-CC after surgical resection. Methods: A total of 1041 eligible patients with pathological diagnosis of cHCC-CC (n=135), HCC (n=698) and ICC (n=208) were enrolled in this study. Univariate and multivariate Cox analysis were applied for assessing important risk factors. cHCC-CC were further 1:1 matched with HCC and ICC on important clinical risk factors. Survival curves of matched and unmatched cohorts were depicted by Kaplan-Meier method with log-rank test. Results: Patients with cHCC-CC had similar rate of sex, age and cirrhosis with HCC (p<0.05) and comparable incidence of hepatitis B or C with ICC (p=0.197). Patients of cHCC-CC had intermediate prognosis between HCC and ICC, with median overall survival (OS) time of cHCC-CC, HCC and ICC of 20.5 months, 35.7 months and 11.6 months (p<0.001). In matched cohorts, the OS of cHCC-CC were worse than HCC (p<0.001) but comparable with ICC (p=0.06), while the disease-free survival (DFS) of cHCC-CC was worse than HCC but better than ICC (p<0.05). And lymph node infiltration and postoperative transarterial chemoembolization (TACE) were independent risk factors of cHCC-CC associated with prognosis. Conclusion: The long term survival of cHCC-CC was worse than HCC but comparable with ICC when matched on albumin level, tumor size, lymph node infiltration, tumor stage and margin. Presence of lymph node infiltration and no postoperative TACE were associated with poor prognosis of cHCC-CC.

Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

OBJECTIVE

To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features.

METHODS

This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors.

RESULTS

Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI.

CONCLUSIONS

Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions.

KEY POINTS

• Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC. • With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.